Active learning for clinical text classification: is it better than random sampling?

ABSTRACT:

Objective

This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks.

Design

Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results.

Measurements

Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with tha

SUBMITTER: Figueroa RL 

PROVIDER: S-EPMC3422824 | biostudies-literature | 2012 Sep-Oct

REPOSITORIES: biostudies-literature